Some faults in manufacturing systems that are evident in event-based data cannot be detected by existing solutions, including systems for which limited information is known. An anomaly detection solution that identifies anomalies in eventbased data using model generation is presented. The solution is based on knowledge of events and resources of the system and generates a set of Petri Net models to detect the anomalies. An example application of this solution is presented for a Ford machining cell that has been experiencing a gantry waiting problem. The anomaly detection solution is able to accurately identify the gantry waiting anomaly and another anomaly that occurred right before the gantry waiting issue, indicating a possible cause.